TD9

idée de plot

surface acheté / surface utilisé
nbr lot sur une carte
surface / valeur foncière

1-Importation des différents modules

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
#!pip install pygal_maps_fr
#import pygal_maps_fr
#!pip install cairosvg
!pip install plotly
!pip install folium
import json
import folium
import plotly.express as px
geo = json.load(open("departements.geojson"))

Requirement already satisfied: plotly in c:\users\azizm\anaconda3\lib\site-packages (4.14.3)
Requirement already satisfied: retrying>=1.3.3 in c:\users\azizm\anaconda3\lib\site-packages (from plotly) (1.3.3)
Requirement already satisfied: six in c:\users\azizm\anaconda3\lib\site-packages (from plotly) (1.15.0)
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Requirement already satisfied: numpy in c:\users\azizm\anaconda3\lib\site-packages (from folium) (1.19.2)
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Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in c:\users\azizm\anaconda3\lib\site-packages (from requests->folium) (1.25.11)
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création de la dataframe et modification des noms des colonnes pour faciliter l'utilisation

df = pd.read_csv('valeursfoncieres-2020.txt',sep='|' ) #creation de la DataFrame
df.drop(df.iloc[:, :7], inplace = True, axis = 1) #on remarque que les 7 premières colonnes ne servent à rien il n'y a pas de données.
df.columns = [c.replace('/', '_') for c in df.columns] #on remplace les '/' pour pouvoir appeler la colonne plus facilement.
df.columns = [c.replace(' ', '_') for c in df.columns] #pareil avec les espaces 
df['Valeur_fonciere'] = df['Valeur_fonciere'].replace({',' : '.'},regex=True) #on remplace les ',' dans la colonne valeur fonciere pour pouvoir la convertir en float
df['Valeur_fonciere'] = df['Valeur_fonciere'].replace({'NaN' : '0'},regex=True)
df.Valeur_fonciere = df.Valeur_fonciere.apply(lambda x :float(x)) #conversion en float
df.Code_departement = df.Code_departement.apply(lambda x :str(x)) 

C:\Users\Azizm\anaconda3\lib\site-packages\IPython\core\interactiveshell.py:3146: DtypeWarning: Columns (14,18,23,24,26,28,31,33,41) have mixed types.Specify dtype option on import or set low_memory=False.
  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,

carte de France

paris = folium.Map(location = [48.856578, 2.351828], zoom_start = 5)
paris

Make this Notebook Trusted to load map: File -> Trust Notebook

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2459560 entries, 0 to 2459559
Data columns (total 36 columns):
 #   Column                      Dtype  
---  ------                      -----  
 0   No_disposition              int64  
 1   Date_mutation               object 
 2   Nature_mutation             object 
 3   Valeur_fonciere             float64
 4   No_voie                     float64
 5   B_T_Q                       object 
 6   Type_de_voie                object 
 7   Code_voie                   object 
 8   Voie                        object 
 9   Code_postal                 float64
 10  Commune                     object 
 11  Code_departement            object 
 12  Code_commune                int64  
 13  Prefixe_de_section          float64
 14  Section                     object 
 15  No_plan                     int64  
 16  No_Volume                   object 
 17  1er_lot                     object 
 18  Surface_Carrez_du_1er_lot   object 
 19  2eme_lot                    object 
 20  Surface_Carrez_du_2eme_lot  object 
 21  3eme_lot                    object 
 22  Surface_Carrez_du_3eme_lot  object 
 23  4eme_lot                    float64
 24  Surface_Carrez_du_4eme_lot  object 
 25  5eme_lot                    float64
 26  Surface_Carrez_du_5eme_lot  object 
 27  Nombre_de_lots              int64  
 28  Code_type_local             float64
 29  Type_local                  object 
 30  Identifiant_local           float64
 31  Surface_reelle_bati         float64
 32  Nombre_pieces_principales   float64
 33  Nature_culture              object 
 34  Nature_culture_speciale     object 
 35  Surface_terrain             float64
dtypes: float64(11), int64(4), object(21)
memory usage: 675.5+ MB

geo.keys()
geo["type"]
len(geo["features"])
geo["features"][0].keys()
geo["features"][0]['type']
geo["features"][0]['properties']

{'code': '02', 'nom': 'Aisne'}

df_departement = df.groupby(["Code_departement"]).count().reset_index()
df_departement.head(10)

	Code_departement	No_disposition	Date_mutation	Nature_mutation	Valeur_fonciere	No_voie	B_T_Q	Type_de_voie	Code_voie	Voie	...	Surface_Carrez_du_5eme_lot	Nombre_de_lots	Code_type_local	Type_local	Identifiant_local	Surface_reelle_bati	Nombre_pieces_principales	Nature_culture	Nature_culture_speciale	Surface_terrain
0	1	28478	28478	28478	27781	12267	759	11561	28222	28222	...	1	28478	12149	12149	0	12120	12120	21198	475	21198
1	10	11879	11879	11879	11661	6278	822	6339	11859	11858	...	6	11879	5909	5909	0	5899	5899	9516	595	9516
2	11	26953	26953	26953	26914	12217	908	11086	26859	26858	...	1	26953	11832	11832	0	11820	11820	22213	1156	22213
3	12	1888	1888	1888	1885	768	67	550	1882	1882	...	0	1888	722	722	0	715	715	1595	277	1595
4	13	49004	49004	49004	47323	36722	2505	37249	47461	47461	...	9	49004	32810	32810	0	32786	32786	18486	1282	18486
5	14	12356	12356	12356	12320	8753	363	7902	12282	12282	...	3	12356	8172	8172	0	8162	8162	8207	279	8207
6	15	4871	4871	4871	4859	1604	94	1127	4865	4865	...	1	4871	1519	1519	0	1519	1519	4423	262	4423
7	16	27702	27702	27702	27524	9282	493	8092	27697	27695	...	0	27702	8564	8564	0	8547	8547	26520	676	26520
8	17	27299	27299	27299	27191	13711	1259	14075	27160	27160	...	0	27299	12344	12344	0	12339	12339	23183	709	23183
9	18	19445	19445	19445	19354	9183	934	8266	19441	19439	...	4	19445	8396	8396	0	8378	8378	17897	511	17897

10 rows × 36 columns

df.groupby(['Code_departement']).No_disposition.count()

Code_departement
1      28478
10     11879
11     26953
12      1888
13     49004
       ...  
95     46746
971     1254
972     2909
973     3295
974     7273
Name: No_disposition, Length: 97, dtype: int64

dfd = df.groupby(['Code_departement']).sum().reset_index()
dfd["Surface_terrain"] = dfd["Surface_terrain"]/100000
dfd["Surface_reelle_bati"] = dfd["Surface_reelle_bati"]/10000
dfd["Valeur_fonciere"] = dfd["Valeur_fonciere"]/1000000
dfd.head(10)

	Code_departement	No_disposition	Valeur_fonciere	No_voie	Code_postal	Code_commune	Prefixe_de_section	No_plan	4eme_lot	5eme_lot	Nombre_de_lots	Code_type_local	Identifiant_local	Surface_reelle_bati	Nombre_pieces_principales	Surface_terrain
0	1	31833	13665.308522	11759139.0	38425200.0	6137678	288124.0	17286890	9261.0	6256.0	8814	23724.0	0.0	120.5383	32297.0	485.24793
1	10	13658	1918.586148	1285468.0	121474620.0	3034128	39828.0	4266704	1094.0	283.0	3128	11340.0	0.0	55.9896	15334.0	301.21166
2	11	28441	4328.982766	9341321.0	303443100.0	5640346	23170.0	11220431	389.0	208.0	5230	21133.0	0.0	96.6430	30709.0	697.68864
3	12	2055	288.207312	1563093.0	23255300.0	301834	10947.0	861022	60.0	88.0	366	1424.0	0.0	5.5478	1668.0	59.97740
4	13	50695	35622.010618	54291111.0	628374676.0	5724566	17810316.0	11997349	31357.0	26391.0	36640	71704.0	0.0	228.2023	69323.0	317.98038
5	14	12913	3871.806011	16203223.0	176891764.0	5303511	629529.0	3140413	1478.0	504.0	5059	15181.0	0.0	63.4164	22166.0	326.73695
6	15	5559	533.061673	3118581.0	74495220.0	602818	31713.0	1644380	173.0	22.0	589	2778.0	0.0	15.3335	4019.0	211.79762
7	16	32864	4384.690976	8998711.0	451448920.0	5048607	463476.0	12853477	420.0	87.0	1289	13157.0	0.0	97.2291	26994.0	759.73659
8	17	28814	5423.174060	7312982.0	470855552.0	7095749	53866.0	12665870	545.0	240.0	4796	20302.0	0.0	126.5121	35492.0	515.27883
9	18	20795	4953.839538	3482009.0	354708750.0	2684882	0.0	6330169	1351.0	810.0	2164	14423.0	0.0	77.9886	23315.0	1010.55128

dfd.info

<bound method DataFrame.info of    Code_departement  No_disposition  Valeur_fonciere     No_voie  \
0                 1           31833     1.366531e+10  11759139.0   
1                10           13658     1.918586e+09   1285468.0   
2                11           28441     4.328983e+09   9341321.0   
3                12            2055     2.882073e+08   1563093.0   
4                13           50695     3.562201e+10  54291111.0   
..              ...             ...              ...         ...   
92               95           48760     3.567059e+11  19822484.0   
93              971            1322     2.024633e+09   2048815.0   
94              972            3014     4.697442e+09   8413534.0   
95              973            3364     5.301502e+09   2676769.0   
96              974            7838     8.788098e+09   1340541.0   

     Code_postal  Code_commune  Prefixe_de_section   No_plan  4eme_lot  \
0   3.842520e+07       6137678            288124.0  17286890    9261.0   
1   1.214746e+08       3034128             39828.0   4266704    1094.0   
2   3.034431e+08       5640346             23170.0  11220431     389.0   
3   2.325530e+07        301834             10947.0    861022      60.0   
4   6.283747e+08       5724566          17810316.0  11997349   31357.0   
..           ...           ...                 ...       ...       ...   
92  4.359156e+09      15314964               518.0  20674044   19188.0   
93  1.212264e+08         20475                 0.0    669121       0.0   
94  2.795263e+08         47789                 0.0   1884284     773.0   
95  3.112672e+08         23798                 0.0   2438166      37.0   
96  6.992740e+08         99937                 0.0   5960128      49.0   

    5eme_lot  Nombre_de_lots  Code_type_local  Identifiant_local  \
0     6256.0            8814          23724.0                0.0   
1      283.0            3128          11340.0                0.0   
2      208.0            5230          21133.0                0.0   
3       88.0             366           1424.0                0.0   
4    26391.0           36640          71704.0                0.0   
..       ...             ...              ...                ...   
92   12177.0           29079          64268.0                0.0   
93       0.0             457           1643.0                0.0   
94     748.0            1221           4826.0                0.0   
95      59.0             819           3125.0                0.0   
96       4.0            2335           9498.0                0.0   

    Surface_reelle_bati  Nombre_pieces_principales  Surface_terrain  
0             1205383.0                    32297.0        485.24793  
1              559896.0                    15334.0        301.21166  
2              966430.0                    30709.0        697.68864  
3               55478.0                     1668.0         59.97740  
4             2282023.0                    69323.0        317.98038  
..                  ...                        ...              ...  
92            2106140.0                    67806.0        510.61457  
93              63102.0                     2290.0         35.26584  
94             144053.0                     5237.0         43.85400  
95             179786.0                     4994.0        527.40052  
96             307938.0                    12834.0        164.79942  

[97 rows x 16 columns]>

france = folium.Map(location = [48.856578, 2.351828], zoom_start = 5)
france.choropleth(geo, key_on = "feature.properties.code",
                 data = dfd, columns = ["Code_departement", "Surface_terrain"],
                 fill_color = "Spectral"
                )
france

C:\Users\Azizm\anaconda3\lib\site-packages\folium\folium.py:409: FutureWarning: The choropleth  method has been deprecated. Instead use the new Choropleth class, which has the same arguments. See the example notebook 'GeoJSON_and_choropleth' for how to do this.
  warnings.warn(

Make this Notebook Trusted to load map: File -> Trust Notebook

france = folium.Map(location = [48.856578, 2.351828], zoom_start = 5)
france.choropleth(geo, key_on = "feature.properties.code",
                 data = dfd, columns = ["Code_departement", "Surface_reelle_bati"],
                 fill_color = "Spectral"
                )
france

Make this Notebook Trusted to load map: File -> Trust Notebook

france = folium.Map(location = [48.856578, 2.351828], zoom_start = 5)
france.choropleth(geo, key_on = "feature.properties.code",
                 data = dfd, columns = ["Code_departement", "Valeur_fonciere"],
                 fill_color = "Spectral"
                )
france

C:\Users\Azizm\anaconda3\lib\site-packages\folium\folium.py:409: FutureWarning: The choropleth  method has been deprecated. Instead use the new Choropleth class, which has the same arguments. See the example notebook 'GeoJSON_and_choropleth' for how to do this.
  warnings.warn(

Make this Notebook Trusted to load map: File -> Trust Notebook

paris = folium.Map(location = [48.856578, 2.351828], zoom_start = 5)
folium.Choropleth(
    geo_data=geo,
    name="choropleth",
    data=df,
    columns=["Code_departement", "Valeur_fonciere"],
    key_on="feature.properties.code",
    fill_color="YlGn",
    fill_opacity=0.7,
    line_opacity=0.2,
    legend_name="Unemployment Rate (%)",
).add_to(paris)

folium.LayerControl().add_to(paris)

paris

Make this Notebook Trusted to load map: File -> Trust Notebook

df.Code_departement.unique()

array(['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12',
       '13', '14', '15', '16', '17', '18', '19', '21', '22', '23', '24',
       '25', '26', '27', '28', '29', '2A', '2B', '30', '31', '32', '33',
       '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44',
       '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55',
       '56', '58', '59', '60', '61', '62', '63', '64', '65', '66', '69',
       '70', '71', '72', '73', '74', '76', '77', '78', '79', '80', '81',
       '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92',
       '93', '94', '95', '971', '972', '973', '974', '75'], dtype=object)

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2459560 entries, 0 to 2459559
Data columns (total 36 columns):
 #   Column                      Dtype  
---  ------                      -----  
 0   No_disposition              int64  
 1   Date_mutation               object 
 2   Nature_mutation             object 
 3   Valeur_fonciere             float64
 4   No_voie                     float64
 5   B_T_Q                       object 
 6   Type_de_voie                object 
 7   Code_voie                   object 
 8   Voie                        object 
 9   Code_postal                 float64
 10  Commune                     object 
 11  Code_departement            object 
 12  Code_commune                int64  
 13  Prefixe_de_section          float64
 14  Section                     object 
 15  No_plan                     int64  
 16  No_Volume                   object 
 17  1er_lot                     object 
 18  Surface_Carrez_du_1er_lot   object 
 19  2eme_lot                    object 
 20  Surface_Carrez_du_2eme_lot  object 
 21  3eme_lot                    object 
 22  Surface_Carrez_du_3eme_lot  object 
 23  4eme_lot                    float64
 24  Surface_Carrez_du_4eme_lot  object 
 25  5eme_lot                    float64
 26  Surface_Carrez_du_5eme_lot  object 
 27  Nombre_de_lots              int64  
 28  Code_type_local             float64
 29  Type_local                  object 
 30  Identifiant_local           float64
 31  Surface_reelle_bati         float64
 32  Nombre_pieces_principales   float64
 33  Nature_culture              object 
 34  Nature_culture_speciale     object 
 35  Surface_terrain             float64
dtypes: float64(11), int64(4), object(21)
memory usage: 675.5+ MB

france = folium.Map(location = [46.603354, 1.8883335], zoom_start = 6)
france.choropleth(geo, key_on = "feature.properties.nom",
                 data = dfg, columns = ["","Surface_reelle_bati"]
                 ,fill_color = "Spectral")
france

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-5-b5f5598fb678> in <module>
      1 france = folium.Map(location = [46.603354, 1.8883335], zoom_start = 6)
----> 2 france.choropleth(geo, key_on = "feature.properties.nom",
      3                  data = dfg, columns = ["","Surface_reelle_bati"]
      4                  ,fill_color = "Spectral")
      5 france

NameError: name 'geo' is not defined

departement = f"{'https://france-geojson.gregoiredavid.fr/repo/departements.geojson'}"
donnee = df.groupby(['Code_departement']).sum()
folium.Choropleth(
    geo_data=departement,
    name="choropleth",
    data=donnee,
    columns=["No_disposition","Valeur_fonciere"],
    key_on="geometry",
    fill_color="YlGn",
    fill_opacity=0.7,
    line_opacity=0.2,
    legend_name="",
).add_to(paris)

paris

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-22-17bbf2f4cf65> in <module>
      1 departement = f"{'https://france-geojson.gregoiredavid.fr/repo/departements.geojson'}"
      2 donnee = df.groupby(['Code_departement']).sum()
----> 3 folium.Choropleth(
      4     geo_data=departement,
      5     name="choropleth",

~\anaconda3\lib\site-packages\folium\features.py in __init__(self, geo_data, data, columns, key_on, bins, fill_color, nan_fill_color, fill_opacity, nan_fill_opacity, line_color, line_weight, line_opacity, name, legend_name, overlay, control, show, topojson, smooth_factor, highlight, **kwargs)
   1287                 smooth_factor=smooth_factor)
   1288         else:
-> 1289             self.geojson = GeoJson(
   1290                 geo_data,
   1291                 style_function=style_function,

~\anaconda3\lib\site-packages\folium\features.py in __init__(self, data, style_function, highlight_function, name, overlay, control, show, smooth_factor, tooltip, embed, popup, zoom_on_click, marker)
    502             self.convert_to_feature_collection()
    503             if self.style:
--> 504                 self._validate_function(style_function, 'style_function')
    505                 self.style_function = style_function
    506                 self.style_map = {}

~\anaconda3\lib\site-packages\folium\features.py in _validate_function(self, func, name)
    567         """
    568         test_feature = self.data['features'][0]
--> 569         if not callable(func) or not isinstance(func(test_feature), dict):
    570             raise ValueError('{} should be a function that accepts items from '
    571                              'data[\'features\'] and returns a dictionary.'

~\anaconda3\lib\site-packages\folium\features.py in style_function(x)
   1265 
   1266         def style_function(x):
-> 1267             color, opacity = color_scale_fun(x)
   1268             return {
   1269                 'weight': line_weight,

~\anaconda3\lib\site-packages\folium\features.py in color_scale_fun(x)
   1250                     raise ValueError("key_on `{!r}` not found in GeoJSON.".format(key_on))
   1251 
-> 1252                 if key_of_x not in color_data.keys():
   1253                     return nan_fill_color, nan_fill_opacity
   1254 

TypeError: unhashable type: 'dict'

df.groupby(['Code_departement']).sum()

	No_disposition	Valeur_fonciere	No_voie	Code_postal	Code_commune	Prefixe_de_section	No_plan	4eme_lot	5eme_lot	Nombre_de_lots	Code_type_local	Identifiant_local	Surface_reelle_bati	Nombre_pieces_principales	Surface_terrain
Code_departement
1	31833	1.366531e+10	11759139.0	3.842520e+07	6137678	288124.0	17286890	9261.0	6256.0	8814	23724.0	0.0	1205383.0	32297.0	48524793.0
10	13658	1.918586e+09	1285468.0	1.214746e+08	3034128	39828.0	4266704	1094.0	283.0	3128	11340.0	0.0	559896.0	15334.0	30121166.0
11	28441	4.328983e+09	9341321.0	3.034431e+08	5640346	23170.0	11220431	389.0	208.0	5230	21133.0	0.0	966430.0	30709.0	69768864.0
12	2055	2.882073e+08	1563093.0	2.325530e+07	301834	10947.0	861022	60.0	88.0	366	1424.0	0.0	55478.0	1668.0	5997740.0
13	50695	3.562201e+10	54291111.0	6.283747e+08	5724566	17810316.0	11997349	31357.0	26391.0	36640	71704.0	0.0	2282023.0	69323.0	31798038.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
95	48760	3.567059e+11	19822484.0	4.359156e+09	15314964	518.0	20674044	19188.0	12177.0	29079	64268.0	0.0	2106140.0	67806.0	51061457.0
971	1322	2.024633e+09	2048815.0	1.212264e+08	20475	0.0	669121	0.0	0.0	457	1643.0	0.0	63102.0	2290.0	3526584.0
972	3014	4.697442e+09	8413534.0	2.795263e+08	47789	0.0	1884284	773.0	748.0	1221	4826.0	0.0	144053.0	5237.0	4385400.0
973	3364	5.301502e+09	2676769.0	3.112672e+08	23798	0.0	2438166	37.0	59.0	819	3125.0	0.0	179786.0	4994.0	52740052.0
974	7838	8.788098e+09	1340541.0	6.992740e+08	99937	0.0	5960128	49.0	4.0	2335	9498.0	0.0	307938.0	12834.0	16479942.0

97 rows × 15 columns

2- Lecture du document

Je l'ai aussi sous forme de csv mais le nombre de données est beaucoup trop importante du coup on passe sur une lecture du fichier txt avec le séparateur '|'. Si jamais vous voulez utiliser l'URL du document lien : Et le csv

3-Simplification de la datafame

j'ai constaté que les 7 premières colonnes étaient toujours vide du coup je les enleve pour plus de lisibilité si jamais je me trompe dites le moi.

4-Affichage de la dataframe

df.head(10)

	No_disposition	Date_mutation	Nature_mutation	Valeur_fonciere	No_voie	B_T_Q	Type_de_voie	Code_voie	Voie	Code_postal	...	Surface_Carrez_du_5eme_lot	Nombre_de_lots	Code_type_local	Type_local	Identifiant_local	Surface_reelle_bati	Nombre_pieces_principales	Nature_culture	Nature_culture_speciale	Surface_terrain
0	1	07/01/2020	Vente	8000.0	NaN	NaN	NaN	B063	FORTUNAT	1250.0	...	NaN	0	NaN	NaN	NaN	NaN	NaN	T	NaN	1061.0
1	1	02/01/2020	Vente	2175.0	NaN	NaN	NaN	B124	TERRES DES CINQ SAULES	1290.0	...	NaN	0	NaN	NaN	NaN	NaN	NaN	BT	NaN	85.0
2	1	02/01/2020	Vente	2175.0	NaN	NaN	NaN	B006	BOIS DU CHAMP RION	1290.0	...	NaN	0	NaN	NaN	NaN	NaN	NaN	T	NaN	1115.0
3	1	02/01/2020	Vente	2175.0	NaN	NaN	NaN	B025	EN COROBERT	1290.0	...	NaN	0	NaN	NaN	NaN	NaN	NaN	T	NaN	1940.0
4	1	02/01/2020	Vente	2175.0	NaN	NaN	NaN	B124	TERRES DES CINQ SAULES	1290.0	...	NaN	0	NaN	NaN	NaN	NaN	NaN	T	NaN	1148.0
5	1	02/01/2020	Vente	2175.0	NaN	NaN	NaN	B124	TERRES DES CINQ SAULES	1290.0	...	NaN	0	NaN	NaN	NaN	NaN	NaN	T	NaN	2960.0
6	1	07/01/2020	Vente	75000.0	NaN	NaN	RUE	0064	DE LA CHARTREUSE	1960.0	...	NaN	0	NaN	NaN	NaN	NaN	NaN	AB	NaN	610.0
7	1	07/01/2020	Vente	123.0	NaN	NaN	NaN	B041	CHAMP PORTIER	1370.0	...	NaN	0	NaN	NaN	NaN	NaN	NaN	S	NaN	55.0
8	1	07/01/2020	Vente	123.0	NaN	NaN	NaN	B041	CHAMP PORTIER	1370.0	...	NaN	0	NaN	NaN	NaN	NaN	NaN	S	NaN	68.0
9	1	09/01/2020	Vente	72000.0	NaN	NaN	NaN	B034	CHAMPEL	1270.0	...	NaN	0	NaN	NaN	NaN	NaN	NaN	J	NaN	328.0

10 rows × 36 columns

Renommer les colonnes pour plus de facilité pour leur utilisation

df.Nature_mutation.unique()

array(['Vente', 'Vente terrain à bâtir', 'Echange',
       "Vente en l'état futur d'achèvement", 'Adjudication',
       'Expropriation'], dtype=object)

df["Code_departement"].value_counts()

33     91383
59     84457
44     81755
77     66274
35     63357
       ...  
972     2909
55      2892
12      1888
971     1254
2B       460
Name: Code_departement, Length: 97, dtype: int64

df_un_departement = df.loc[df['Code_departement'] == "25" ].reset_index()
df_un_departement.head(10)

	index	No_disposition	Date_mutation	Nature_mutation	Valeur_fonciere	No_voie	B_T_Q	Type_de_voie	Code_voie	Voie	...	Surface_Carrez_du_5eme_lot	Nombre_de_lots	Code_type_local	Type_local	Identifiant_local	Surface_reelle_bati	Nombre_pieces_principales	Nature_culture	Nature_culture_speciale	Surface_terrain
0	485396	1	03/01/2020	Vente	145000.0	10.0	B	RTE	0090	DE LYON	...	NaN	0	2.0	Appartement	NaN	73.0	2.0	S	NaN	198.0
1	485397	1	03/01/2020	Vente	145000.0	10.0	B	RTE	0090	DE LYON	...	NaN	0	2.0	Appartement	NaN	44.0	2.0	S	NaN	198.0
2	485398	1	03/01/2020	Vente	145000.0	NaN	NaN	NaN	B035	CROIX DU CLOS	...	NaN	0	NaN	NaN	NaN	NaN	NaN	S	NaN	38.0
3	485399	1	06/01/2020	Vente en l'état futur d'achèvement	230000.0	17.0	NaN	RUE	5160	DE VESOUL	...	NaN	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	485400	1	06/01/2020	Vente en l'état futur d'achèvement	230000.0	17.0	NaN	RUE	5160	DE VESOUL	...	NaN	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
5	485401	1	06/01/2020	Vente	62000.0	6.0	NaN	PL	1990	FLORE	...	NaN	2	2.0	Appartement	NaN	72.0	3.0	NaN	NaN	NaN
6	485402	1	07/01/2020	Vente	235000.0	23.0	NaN	RTE	0042	DE LA VALLEE	...	NaN	0	3.0	Dépendance	NaN	0.0	0.0	S	NaN	693.0
7	485403	1	07/01/2020	Vente	235000.0	23.0	NaN	RTE	0042	DE LA VALLEE	...	NaN	0	2.0	Appartement	NaN	85.0	3.0	S	NaN	693.0
8	485404	1	07/01/2020	Vente	235000.0	23.0	NaN	RTE	0042	DE LA VALLEE	...	NaN	0	2.0	Appartement	NaN	65.0	3.0	S	NaN	693.0
9	485405	1	07/01/2020	Vente	235000.0	23.0	NaN	RTE	0042	DE LA VALLEE	...	NaN	0	2.0	Appartement	NaN	65.0	3.0	S	NaN	693.0

10 rows × 37 columns

df_un_departement['Nature_mutation'].value_counts().plot.pie()

<AxesSubplot:ylabel='Nature_mutation'>

df_departement = df.groupby(['Code_departement'])
df_departement.Valeur_fonciere.sum()
df_departement.Valeur_fonciere.sum().plot.pie()

<AxesSubplot:ylabel='Valeur_fonciere'>

# color pallette
cnf = '#393e46' # confirmed - grey
dth = '#ff2e63' # death - red
rec = '#21bf73' # recovered - cyan
act = '#fe9801' # active case - yellow

temp = df.groupby('Date_mutation').sum().reset_index()
#Nombre_pieces_principales
# adding two more columns
temp['Valeur foncière par mètre carré de terrain'] = round(temp['Valeur_fonciere']/temp['Surface_terrain'], 3)
temp['Valeur foncière par nombre de pièces principales'] = round(temp['Valeur_fonciere']/temp['Nombre_pieces_principales'], 3)

temp = temp.melt(id_vars='Date_mutation', value_vars=['Valeur foncière par mètre carré de terrain','Valeur foncière par nombre de pièces principales'], 
                 var_name='Ratio', value_name='Valeur')

fig = px.line(temp, x="Date_mutation", y="Valeur", color='Ratio', log_y=True, 
              title='Evolution de la valeur foncière par mètre carré de terrain', color_discrete_sequence=[dth,rec])
fig.show()

temp = df.groupby('Date_mutation')['Valeur_fonciere'].sum().reset_index()
temp = temp.melt(id_vars="Date_mutation", value_vars=['Valeur_fonciere'],
                 var_name='Variable', value_name='Valeur_fonciere')
temp.head()

fig = px.area(temp, x="Date_mutation", y="Valeur_fonciere", color='Variable',
             title='Valeur foncière en fonction du temps', color_discrete_sequence = [rec])
fig.show()

<ipython-input-14-7649795a38c4>:2: FutureWarning:

This dataframe has a column name that matches the 'value_name' column name of the resultiing Dataframe. In the future this will raise an error, please set the 'value_name' parameter of DataFrame.melt to a unique name.